Data, Data, Data, Collection Methodologies and Quantitative vs. Qualitative
DESN2003: Research for Innovation, Week Seven (Part 1)
Hongshan Guo
Introducting Data before its Collection
Types of Research Data Collection (Source)
Primary Data Source
1st hand information
not changed by any individual
not published yet
directly collected by authors
Secondary Source
published data
what literature review is based on
reviewed by authors(you)
Examples of data collection methods by category
Primary Data Collection Examples
Questionnaires
Interviews
Focus Group Interviews
Observation
participant/non-participant
aware/non-aware
Survey
Statistical Methods
Experimental Methods
Secondary Data Collection Examples
Published Papers/Sources
Databases
Books
General websites
Unpulibhsed personal records
Census data/population statistics
Primary Data Collection Methods: Desining Questionnaires
a set of questions and secure answers from respondants
often analyzed by statsitical methods
consistency in questionnaires make cross-sectional analysis easy
Types of questions designed to measure variables in survey:
Close-end questions
Two-option aka dichotomous scales
More than two options: Nominal-polychromous
Ordinal-Polytomous scale
Continous or bounded types
Open-end questions
setence completion
open-ended questions with free text responses
Polytomous Variables, aka Options for Multiple Choice Questions
Statistical term that refers to a categorical variable with more than two possible categories or levels
Common in: Survey research ,healthcare, market research and education
Characteristics:
Categorical data: limited/distinct values or categories that are mutually exclusive
Always more than two options(binary)
Ordered and unordered (natural hiearchy)
Statistical analyses: e.g. logistic regression, cluster analysis for patterns and trends
Convertable to binary variables
Purpose:
Understanding complex phenomena: patterns and trends in the data collected
Provide insights to better categorization of data
Statistical analyses: Relationships between variables: e.g. logistic regression to predict probability
Market segmentation: e.g. customers to preference, behavior, demographic groups
Polytomous Variable (Continued)
When to use Polytomous Variables
Measuring attitudes or perceptions: for more nuanced perceptions
Putting definitions on categorizzing data: greater variability
Analyzing relationships between variables: job satisfication & job performance
Need to capture complex phenomena
Limitations
Power of explanation limited by available options
e.g. satisfactory survey & number of enrolled students, what about change?
subjectivity from questionnaire design and interpretation of results
response bias
small sample sizes
calculating the appropriate size of population needed
amount of respondants needed to reach statistical significance (reject null hypothesis, not covered by current class, leverage sample size calculator online)
Primary Data Collection: Designing Questionnaires
Face-to-face, paper-and-pencil or remote, make sure your data collected is: - well-organized and - easily accessible for analysis.
General rules for constructing a questionnaire:
Dos:
questions should be short and simple
provide clear navigation to avoid difficulty in reading and motivate answering
use positive sentences
add open-answer possibility after provideing listed answers
improve reliability by selecting appropriate words
explain importance of the questionnaire
order your questions to solicit the right answers (sensitive to follow concrete/innocuous ones)
Do-nots:
use more than one question (double-barreled) in one item
make assumptions for the respondents
lead the respondant to answers with clues, suggestions and hints
Steps involved in designing a questionnaire
Primary Data Collection: Interviews
Face-to-face and remote (telephone/zoom) interviews and merit/demerits (Kabir, 2016).
Good for complex or sensitive concepts and need detailed and high-status information (Frechtling, 2020).
Types of interviews by structure:
structured interviews: standardized questions that are pre-prepared
semi-structured interviews: conducted based on guide but goes beyond list of questions
Several years ago, a group of students at University of Central Arkansas conducted a study in which they observed the rate at which cars failed to stop at a campus stop sign and recorded whether the car had a student parking decal or a faculty/staff parking decal. This is obviously not fitting for Hong Kong context. Let’s perhaps picture a study of the rate of jay-walking at a traffic light instead - and record whether the pedestrian who crossed is a student/staff/tourist/local resident. Use what we have covered today to answer questions 1-7:
Which method of observation would be best? Justify your answer. Hint: back to participant/direct observations.
How would you schedule observations?
Define the categories of behavior that you would observe
Describe how you would optimize and measure the reliability of observations, including the use of independent observers and calculation of interobserver agreement.
Describe how you could use equipment for observation rather than human observers, what are the advantages and disadvantages?
Describe how you might use public records to answer the same research question. What might be some limitations of this approach
Describe how you might use a survey method to answer the same research question. What might be some limitations of this approach?
References
Frechtling, J. (2002). An overview of quantitative and qualitative data collection methods The 2002 user-friendly handbook for project evaluation (pp. 43-62).
Hox, J. J., & Boeije, H. R. (2005). Data collection, primary versus secondary Encyclopedia of social Measurement (Vol. 1): Elsevier.
Data collection challenges (2005).
Kabir, S. M. S. (2016). Methods Of Data Collection Basic Guidelines for Research: An Introductory Approach for All Disciplines (first ed., pp. 201-275).
Olsen, W. (2012). Data collecti on: Key debates and methods in social research (Vol. 1): Sage.
Pandey, P., & Pandey, M. M. (2015). Research Methodology: Tools and Techniques (Vol. 1). Romania: Bridge Center.
Rimando, M., Brace, A. M., Namageyo-Funa, A., Parr, T. L., Sealy, D.-A., Davis, T. L., . . . Christiana, R.W. (2015). Data collection challenges and recommendations for early career researchers. The Qualitative Report, 20 (12), 2025-2036.
Taherdoost, H. (2016a). How to design and create an effective survey/questionnaire; A step by step guide. International Journal of Academic Research in Management (IJARM), 5 (4), 37-41.
Taherdoost, H. (2016b). Measurement and scaling techniques in research methodology; survey/questionnaire development. International Journal of Academic Research in Management (IJARM),6 (1), 1-5.
Taherdoost, H. (2016c). Sampling methods in research methodology; how to choose a sampling technique for research. International Journal of Academic Research in Management (IJARM), 5 (2), 18-27.
Taherdoost, H. (2019). What is the best response scale for survey and questionnaire design; review of different lengths of rating scale/attitude scale/Likert scale. International Journal of Academic Research in Management (IJARM), 8 (1), 1-10.
Taherdoost, H. (2021). Handbook on Research Skills: The Essential Step-By-Step; Guide on How to Do a Research Project (Kindle ed.): Amazon.